Motivation and Problem Statement
Artificial intelligence systems are increasingly embedded in consequential domains (public administration, healthcare, financial services, criminal justice, content moderation) where their outputs directly affect individuals and communities. This widespread deployment has generated a global regulatory response that is substantial in volume but heterogeneous in approach.
The European Union has enacted the first comprehensive, binding AI legislation through the Artificial Intelligence Act (Regulation (EU) 2024/1689). The United States has pursued a combination of executive directives, voluntary technical frameworks, and emergent state-level legislation. China has implemented binding regulations targeting specific AI application categories. Other jurisdictions (Canada, the United Kingdom, Singapore, Japan) occupy distinct positions along the spectrum from binding legislation to voluntary guidance.
This regulatory heterogeneity creates analytical fragmentation, comparison without structure, and practical coordination barriers for organizations operating across borders. A structured comparative framework is needed, one that decomposes AI regulation into analytically meaningful dimensions and enables both descriptive mapping and analytical comparison.
Research Gap
Gap 1: Absence of a common analytical framework
Most comparative studies organize analysis by jurisdiction rather than by regulatory dimension, limiting systematic cross-jurisdictional comparison at the provision level.
Gap 2: Limited operationalization of governance frameworks
The People–Process–Technology triad has been discussed conceptually in AI governance literature (Coglianese, 2023) but has not been operationalized into a formal coding instrument applied systematically across a broad set of frameworks.
Gap 3: Rapidly evolving regulatory landscape
The EU AI Act entered into force in August 2024, Japan enacted the AI Promotion Act in 2025, and the Colorado AI Act takes effect in 2026. Existing surveys do not capture these developments.
Gap 4: Insufficient attention to regulatory interdependencies
Few studies examine how provisions in one dimension condition or constrain provisions in another—for example, how AI system classification determines the intensity of human oversight requirements.
Research Questions
How do major AI regulatory frameworks distribute governance requirements across the People, Processes, and Platforms dimensions, and what configuration patterns emerge?
What governance philosophies—risk-based, rights-based, innovation-first, state-directed, promotional—underpin different frameworks, and how do they shape PPP emphasis?
On which PPP sub-dimensions do global AI regulatory frameworks exhibit the greatest convergence, and on which do they diverge most significantly?
How do interdependencies between PPP dimensions manifest within and across regulatory regimes?
What regulatory gaps emerge from the comparative analysis, and what do they imply for international harmonization?
Expected Contributions
- A validated PPP coding framework with 17 sub-dimensions for comparative AI regulatory analysis, with 87.6% AI-human agreement across 170 classifications.
- A systematic provision-level comparative matrix of global AI governance covering 12 instruments across 10 jurisdictions as of early 2026.
- Identification of two structural determinants of regulatory coherence: classification as a cross-dimensional trigger and the institutional creation threshold.
- Five testable propositions about the relationship between regulatory architecture and governance outcomes.
Key References
- Baldwin, R., Cave, M., & Lodge, M. (2012). Understanding Regulation (2nd ed.). Oxford University Press.
- Coglianese, C. (2023). A People-and-Processes Approach to AI Governance. Administrative and Regulatory Law News, ABA.
- European Commission. (2024). Regulation (EU) 2024/1689: The Artificial Intelligence Act.
- Leavitt, H. J. (1965). Applied Organizational Change in Industry. In March (Ed.), Handbook of Organizations.
- NIST. (2023). AI Risk Management Framework (AI RMF 1.0). NIST AI 100-1.
- OECD. (2019, updated 2024). OECD Principles on Artificial Intelligence.
- UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence.